Wstęp

Największą przeszkodą w analizowanym zbiorze danych był jego rozmiar.Od samego wczytywania danych, poprzez proces czyszczenia, aż do budowania klasyfikatora należało pamiętać o skończonej ilości czasu i pamięci jaką się dysponuje. Pierwsze próby trenowania klasyfikatorów takich jak kNN czy Random Forest zakończyły się niepowodzeniem z powodu zbyt długiego czasu wykonania. Aby przyspieszyć proces najpierw wybrałem prostszy algorytm regresji liniowej i usunąłem ze zbioru danych atrybuty o niewielkim odstępie międzykwartylowym, zakładając że niewiele wniosą do klasyfikacji czy regresji. Następnie zacząłem usuwać po jednym atrybucie z każdej pary atrybutów mocno skorelowanych. W ten sposób ograniczyłem zbiór danych do 8 atrybutów i 3 etykiet. Pozwoliło to wykorzystać algorytm Random Forest do klasyfikacji, a także bardzo szybko liczyć regresję liniową. Niestety jakość modeli, który powstał w ten sposób pozostawia wiele do życzenia. Próba przybliżenia liczby elektronów czy atomów skończyła się utworzeniem modelu o dość niskim współczynniku R^2 (ok. 0.43), co może wskazywać na nieliniowość tego zjawiska lub błędny proces oczyszczania danych. Jeszcze gorzej zakończyła się próba przypisania danej obserwacji do odpowiadającej cząsteczki. Dokładność wyniosła jedynie ok. 27%. Podjąłem dodatkowo próbę zbudowania klasyfikatora, który na wejściu będzie przyjmował wyjście ze zbudowanych wcześniej przeze mnie modeli określających liczbę atomów i elektronów. Niestety ta próba zakończyła się poprawnym sklasyfikowaniem jedynie 16% przykładów.

Wczytanie i wstępne przetwarzanie danych

Wykorzystywanie biblioteki

library(dplyr)
library(ggplot2)
library(tidyr)
library(caret)
library(scales)
library(plotly)
library(knitr)
library(kableExtra)

Zapewnienie powtarzalności analizy

set.seed(23)

Wczytwanie danych z pliku

knownClasses <- c("title"="character", "blob_coverage"="character", "res_coverage"="character", "skeleton_data"="character")
initial <- read.csv(file="C:/Users/wilcz/OneDrive/Pulpit/all_summary/all_summary.csv", header=TRUE, sep=";", nrows = 75000, colClasses=knownClasses)
classes <- sapply(initial, class)
rm(initial)
All_Data <- read.csv(file="C:/Users/wilcz/OneDrive/Pulpit/all_summary/all_summary.csv", header=TRUE, sep=";",colClasses = classes)

Usuwanie nieporządanych res_name

res_names_to_drop <- c("UNK", "UNX", "UNL", "DUM", "N", "BLOB", "ALA", "ARG", "ASN", "ASP", "CYS", "GLN", "GLU", "GLY", "HIS", "ILE", "LEU", "LYS", "MET", "MSE", "PHE", "PRO", "SEC", "SER", "THR", "TRP", "TYR", "VAL", "DA", "DG", "DT", "DC", "DU", "A", "G", "T", "C", "U", "HOH", "H20", "WAT")
All_Data <- All_Data %>% filter(!(res_name %in% res_names_to_drop) )

Uzupełnianie brakujących wartości

has_conflicted_res_name <- function(observation) { 
  !is.na(observation$res_name) & observation$name_from_title != as.character(observation$res_name) 
}

tmp_name <- All_Data[, c("title","res_name")]
tmp_name$name_from_title <- sapply(tmp_name$title, function(x) { strsplit(x," ")[[1]][2] })
kable(tmp_name[has_conflicted_res_name(tmp_name),])  %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "600px") #sprawdzenie czy gdzieś występują niespójności w nazwach
title res_name name_from_title 
#nie występują, więc możemy nadpisać kolumnę res_name wartościami z tytulu
kable(head(tmp_name[is.na(tmp_name$res_name),]))%>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "600px") # ZONK sód (NA) jest traktowany jako wartość pusta! takich wartości jest 10K
title res_name name_from_title
639 4fxz NA 602 A NA NA
640 4fxz NA 603 A NA NA
901 5fkf NA 1108 A NA NA
902 5fkf NA 1105 A NA NA
1038 5j2b NA 404 A NA NA
1039 5j2b NA 405 A NA NA 
levels(tmp_name$res_name) <- c( levels(tmp_name$res_name), "NA")
tmp_name[is.na(tmp_name$res_name) & tmp_name$name_from_title == "NA","res_name"] <- "NA"
kable(summary(All_Data$res_name))%>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "600px") #9.6k NAs
x
SO4 56572
GOL 40606
EDO 30825
NAG 26360
CL 23223
CA 21038
ZN 19826
MG 14779
HEM 11192
PO4 11090
ACT 8096
DMS 6633
IOD 6317
PEG 4987
CLA 4784
K 4706
FAD 4555
NAD 4501
MN 4215
ADP 3819
MLY 3509
NAP 3505
CD 3242
MPD 3221
FMT 2918
MAN 2841
PG4 2768
MES 2697
CU 2353
ATP 2296
COA 2183
1PE 2136
BR 2127
NDP 2106
FMN 2084
EPE 1933
HEC 1917
PGE 1905
TRS 1656
SF4 1647
NI 1637
ACY 1609
FE 1602
NO3 1596
PLP 1594
GDP 1589
SAH 1587
FE2 1560
SEP 1491
CIT 1464
BME 1419
ANP 1404
BOG 1387
C8E 1369
BMA 1335
GSH 1282
LDA 1278
GLC 1219
OLC 1186
ACE 1154
GTP 1133
BGC 1120
AMP 1114
TPO 1106
IPA 1092
P6G 1084
CO 1041
IMD 1039
CSO 1038
FES 1034
GAL 1017
PTR 1000
LLP 992
CME 961
HG 950
SIA 906
MRD 902
KCX 897
SAM 864
HYP 852
BCL 848
LMT 835
FLC 809
CDL 795
CYC 783
ACO 748
NCO 747
UDP 725
BCR 721
PCA 717
SCN 685
MLI 674
CSD 651
FUC 627
NAI 624
TLA 623
NDG 618
CAS 617
(Other) 156807
NA’s 9613 
All_Data$res_name <- tmp_name$res_name
kable(summary(All_Data$res_name))%>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "600px") # 0 NAs - victory!
x
SO4 56572
GOL 40606
EDO 30825
NAG 26360
CL 23223
CA 21038
ZN 19826
MG 14779
HEM 11192
PO4 11090
NA 9613
ACT 8096
DMS 6633
IOD 6317
PEG 4987
CLA 4784
K 4706
FAD 4555
NAD 4501
MN 4215
ADP 3819
MLY 3509
NAP 3505
CD 3242
MPD 3221
FMT 2918
MAN 2841
PG4 2768
MES 2697
CU 2353
ATP 2296
COA 2183
1PE 2136
BR 2127
NDP 2106
FMN 2084
EPE 1933
HEC 1917
PGE 1905
TRS 1656
SF4 1647
NI 1637
ACY 1609
FE 1602
NO3 1596
PLP 1594
GDP 1589
SAH 1587
FE2 1560
SEP 1491
CIT 1464
BME 1419
ANP 1404
BOG 1387
C8E 1369
BMA 1335
GSH 1282
LDA 1278
GLC 1219
OLC 1186
ACE 1154
GTP 1133
BGC 1120
AMP 1114
TPO 1106
IPA 1092
P6G 1084
CO 1041
IMD 1039
CSO 1038
FES 1034
GAL 1017
PTR 1000
LLP 992
CME 961
HG 950
SIA 906
MRD 902
KCX 897
SAM 864
HYP 852
BCL 848
LMT 835
FLC 809
CDL 795
CYC 783
ACO 748
NCO 747
UDP 725
BCR 721
PCA 717
SCN 685
MLI 674
CSD 651
FUC 627
NAI 624
TLA 623
NDG 618
CAS 617
(Other) 156807 
rm(tmp_name)

Rozmiar zbioru i podstawowe statystyki.

kable(dim(All_Data))%>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "600px")
x
585339
412 
kable(summary(All_Data))%>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "600px")
blob_coverage res_coverage 
title </th>
 
pdb_code </th>
 
res_name </th>
 
 res_id </th>
 
chain_id </th>
 blob_volume_coverage blob_volume_coverage_second res_volume_coverage res_volume_coverage_second local_res_atom_count local_res_atom_non_h_count local_res_atom_non_h_occupancy_sum local_res_atom_non_h_electron_sum local_res_atom_non_h_electron_occupancy_sum local_res_atom_C_count local_res_atom_N_count local_res_atom_O_count local_res_atom_S_count dict_atom_non_h_count dict_atom_non_h_electron_sum dict_atom_C_count dict_atom_N_count dict_atom_O_count dict_atom_S_count skeleton_data skeleton_cycle_4 skeleton_diameter skeleton_cycle_6 skeleton_cycle_7 skeleton_closeness_006_008 skeleton_closeness_002_004 skeleton_cycle_3 skeleton_avg_degree skeleton_closeness_004_006 skeleton_closeness_010_012 skeleton_closeness_012_014 skeleton_edges skeleton_radius skeleton_cycle_8_plus skeleton_closeness_020_030 skeleton_deg_5_plus skeleton_closeness_016_018 skeleton_closeness_008_010 skeleton_closeness_018_020 skeleton_average_clustering skeleton_closeness_040_050 skeleton_closeness_014_016 skeleton_center skeleton_closeness_000_002 skeleton_density skeleton_closeness_030_040 skeleton_deg_4 skeleton_deg_0 skeleton_deg_1 skeleton_deg_2 skeleton_deg_3 skeleton_graph_clique_number skeleton_nodes skeleton_cycles skeleton_cycle_5 skeleton_closeness_050_plus skeleton_periphery local_volume local_electrons local_mean local_std local_min local_max local_max_over_std local_skewness local_cut_by_mainchain_volume local_near_cut_count_C local_near_cut_count_other local_near_cut_count_S local_near_cut_count_O local_near_cut_count_N part_00_shape_segments_count part_00_density_segments_count part_00_volume part_00_electrons part_00_mean part_00_std part_00_max part_00_max_over_std part_00_skewness part_00_parts part_00_shape_O3 part_00_shape_O4 part_00_shape_O5 part_00_shape_FL part_00_shape_O3_norm part_00_shape_O4_norm part_00_shape_O5_norm part_00_shape_FL_norm part_00_shape_I1 part_00_shape_I2 part_00_shape_I3 part_00_shape_I4 part_00_shape_I5 part_00_shape_I6 part_00_shape_I1_norm part_00_shape_I2_norm part_00_shape_I3_norm part_00_shape_I4_norm part_00_shape_I5_norm part_00_shape_I6_norm part_00_shape_M000 part_00_shape_CI part_00_shape_E3_E1 part_00_shape_E2_E1 part_00_shape_E3_E2 part_00_shape_sqrt_E1 part_00_shape_sqrt_E2 part_00_shape_sqrt_E3 part_00_density_O3 part_00_density_O4 part_00_density_O5 part_00_density_FL part_00_density_O3_norm part_00_density_O4_norm part_00_density_O5_norm part_00_density_FL_norm part_00_density_I1 part_00_density_I2 part_00_density_I3 part_00_density_I4 part_00_density_I5 part_00_density_I6 part_00_density_I1_norm part_00_density_I2_norm part_00_density_I3_norm part_00_density_I4_norm part_00_density_I5_norm part_00_density_I6_norm part_00_density_M000 part_00_density_CI part_00_density_E3_E1 part_00_density_E2_E1 part_00_density_E3_E2 part_00_density_sqrt_E1 part_00_density_sqrt_E2 part_00_density_sqrt_E3 part_00_shape_Z_7_3 part_00_shape_Z_0_0 part_00_shape_Z_7_0 part_00_shape_Z_7_1 part_00_shape_Z_3_0 part_00_shape_Z_5_2 part_00_shape_Z_6_1 part_00_shape_Z_3_1 part_00_shape_Z_6_0 part_00_shape_Z_2_1 part_00_shape_Z_6_3 part_00_shape_Z_2_0 part_00_shape_Z_6_2 part_00_shape_Z_5_0 part_00_shape_Z_5_1 part_00_shape_Z_4_2 part_00_shape_Z_1_0 part_00_shape_Z_4_1 part_00_shape_Z_7_2 part_00_shape_Z_4_0 part_00_density_Z_7_3 part_00_density_Z_0_0 part_00_density_Z_7_0 part_00_density_Z_7_1 part_00_density_Z_3_0 part_00_density_Z_5_2 part_00_density_Z_6_1 part_00_density_Z_3_1 part_00_density_Z_6_0 part_00_density_Z_2_1 part_00_density_Z_6_3 part_00_density_Z_2_0 part_00_density_Z_6_2 part_00_density_Z_5_0 part_00_density_Z_5_1 part_00_density_Z_4_2 part_00_density_Z_1_0 part_00_density_Z_4_1 part_00_density_Z_7_2 part_00_density_Z_4_0 part_01_shape_segments_count part_01_density_segments_count part_01_volume part_01_electrons part_01_mean part_01_std part_01_max part_01_max_over_std part_01_skewness part_01_parts part_01_shape_O3 part_01_shape_O4 part_01_shape_O5 part_01_shape_FL part_01_shape_O3_norm part_01_shape_O4_norm part_01_shape_O5_norm part_01_shape_FL_norm part_01_shape_I1 part_01_shape_I2 part_01_shape_I3 part_01_shape_I4 part_01_shape_I5 part_01_shape_I6 part_01_shape_I1_norm part_01_shape_I2_norm part_01_shape_I3_norm part_01_shape_I4_norm part_01_shape_I5_norm part_01_shape_I6_norm part_01_shape_M000 part_01_shape_CI part_01_shape_E3_E1 part_01_shape_E2_E1 part_01_shape_E3_E2 part_01_shape_sqrt_E1 part_01_shape_sqrt_E2 part_01_shape_sqrt_E3 part_01_density_O3 part_01_density_O4 part_01_density_O5 part_01_density_FL part_01_density_O3_norm part_01_density_O4_norm part_01_density_O5_norm part_01_density_FL_norm part_01_density_I1 part_01_density_I2 part_01_density_I3 part_01_density_I4 part_01_density_I5 part_01_density_I6 part_01_density_I1_norm part_01_density_I2_norm part_01_density_I3_norm part_01_density_I4_norm part_01_density_I5_norm part_01_density_I6_norm part_01_density_M000 part_01_density_CI part_01_density_E3_E1 part_01_density_E2_E1 part_01_density_E3_E2 part_01_density_sqrt_E1 part_01_density_sqrt_E2 part_01_density_sqrt_E3 part_01_shape_Z_7_3 part_01_shape_Z_0_0 part_01_shape_Z_7_0 part_01_shape_Z_7_1 part_01_shape_Z_3_0 part_01_shape_Z_5_2 part_01_shape_Z_6_1 part_01_shape_Z_3_1 part_01_shape_Z_6_0 part_01_shape_Z_2_1 part_01_shape_Z_6_3 part_01_shape_Z_2_0 part_01_shape_Z_6_2 part_01_shape_Z_5_0 part_01_shape_Z_5_1 part_01_shape_Z_4_2 part_01_shape_Z_1_0 part_01_shape_Z_4_1 part_01_shape_Z_7_2 part_01_shape_Z_4_0 part_01_density_Z_7_3 part_01_density_Z_0_0 part_01_density_Z_7_0 part_01_density_Z_7_1 part_01_density_Z_3_0 part_01_density_Z_5_2 part_01_density_Z_6_1 part_01_density_Z_3_1 part_01_density_Z_6_0 part_01_density_Z_2_1 part_01_density_Z_6_3 part_01_density_Z_2_0 part_01_density_Z_6_2 part_01_density_Z_5_0 part_01_density_Z_5_1 part_01_density_Z_4_2 part_01_density_Z_1_0 part_01_density_Z_4_1 part_01_density_Z_7_2 part_01_density_Z_4_0 part_02_shape_segments_count part_02_density_segments_count part_02_volume part_02_electrons part_02_mean part_02_std part_02_max part_02_max_over_std part_02_skewness part_02_parts part_02_shape_O3 part_02_shape_O4 part_02_shape_O5 part_02_shape_FL part_02_shape_O3_norm part_02_shape_O4_norm part_02_shape_O5_norm part_02_shape_FL_norm part_02_shape_I1 part_02_shape_I2 part_02_shape_I3 part_02_shape_I4 part_02_shape_I5 part_02_shape_I6 part_02_shape_I1_norm part_02_shape_I2_norm part_02_shape_I3_norm part_02_shape_I4_norm part_02_shape_I5_norm part_02_shape_I6_norm part_02_shape_M000 part_02_shape_CI part_02_shape_E3_E1 part_02_shape_E2_E1 part_02_shape_E3_E2 part_02_shape_sqrt_E1 part_02_shape_sqrt_E2 part_02_shape_sqrt_E3 part_02_density_O3 part_02_density_O4 part_02_density_O5 part_02_density_FL part_02_density_O3_norm part_02_density_O4_norm part_02_density_O5_norm part_02_density_FL_norm part_02_density_I1 part_02_density_I2 part_02_density_I3 part_02_density_I4 part_02_density_I5 part_02_density_I6 part_02_density_I1_norm part_02_density_I2_norm part_02_density_I3_norm part_02_density_I4_norm part_02_density_I5_norm part_02_density_I6_norm part_02_density_M000 part_02_density_CI part_02_density_E3_E1 part_02_density_E2_E1 part_02_density_E3_E2 part_02_density_sqrt_E1 part_02_density_sqrt_E2 part_02_density_sqrt_E3 part_02_shape_Z_7_3 part_02_shape_Z_0_0 part_02_shape_Z_7_0 part_02_shape_Z_7_1 part_02_shape_Z_3_0 part_02_shape_Z_5_2 part_02_shape_Z_6_1 part_02_shape_Z_3_1 part_02_shape_Z_6_0 part_02_shape_Z_2_1 part_02_shape_Z_6_3 part_02_shape_Z_2_0 part_02_shape_Z_6_2 part_02_shape_Z_5_0 part_02_shape_Z_5_1 part_02_shape_Z_4_2 part_02_shape_Z_1_0 part_02_shape_Z_4_1 part_02_shape_Z_7_2 part_02_shape_Z_4_0 part_02_density_Z_7_3 part_02_density_Z_0_0 part_02_density_Z_7_0 part_02_density_Z_7_1 part_02_density_Z_3_0 part_02_density_Z_5_2 part_02_density_Z_6_1 part_02_density_Z_3_1 part_02_density_Z_6_0 part_02_density_Z_2_1 part_02_density_Z_6_3 part_02_density_Z_2_0 part_02_density_Z_6_2 part_02_density_Z_5_0 part_02_density_Z_5_1 part_02_density_Z_4_2 part_02_density_Z_1_0 part_02_density_Z_4_1 part_02_density_Z_7_2 part_02_density_Z_4_0 
fo_col </th>
 
 fc_col </th>
 weight_col grid_space solvent_radius solvent_opening_radius resolution_max_limit resolution FoFc_mean 
FoFc_std </th>
 FoFc_square_std 
FoFc_min </th>
 
FoFc_max </th>
 part_step_FoFc_std_min part_step_FoFc_std_max part_step_FoFc_std_step
Length:585339 Length:585339 Length:585339 4xk8 : 868 SO4 : 56572 301 : 19436 A :277258 Min. :0.02004 Min. :0.0000 Min. :0.001884 Min. :0.00000 Min. : 1.0 Min. : 1.00 Min. : -7.38 Min. : 3.0 Min. :-45.91 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 1.00 Min. : 3 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Length:585339 Min. : 0.0000 Min. : 0.00 Min. : 0.00000 Min. : 0.0000 Min. : 0.000 Min. : 0.0000 Min. : 0.0000 Min. :0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.00 Min. : 0.00 Min. : 0.0000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.0000000 Min. : 0.000 Min. : 0.00 Min. : 1.000 Min. : 0.0000 Min. :0.00000 Min. : 0.000 Min. : 0.0000 Min. :0.0000 Min. : 0.000 Min. : 0.00 Min. : 0.000 Min. :1.000 Min. : 1.00 Min. : 0.00 Min. : 0.0000 Min. : 0.000 Min. : 1.000 Min. : 49.25 Min. : 0.0117 Min. :0.0001147 Min. :0.0006606 Min. :0 Min. : 0.00452 Min. : 2.836 Min. :0.001174 Min. : 0.0000 Min. : 0.000 Min. : 0.00000 Min. : 0.0000 Min. : 0.00 Min. : 0.000 Min. : 0.0 Min. : 0.0 Min. : 0.00 Min. : 0.000 Min. :0.0000 Min. :0.00000 Min. : 0.0000 Min. : 0.000 Min. : 0.00000 Min. : 0.000 Min. :1.210e+02 Min. :3.807e+03 Min. :3.378e+04 Min. :7.600e+01 Min. : 0.2306 Min. :0.01756 Min. :0.000426 Min. : 0.00000 Min. :4.800e+02 Min. :5.694e+04 Min. :4.999e+04 Min. :4.200e+01 Min. :2.000e+00 Min. :2.234e+04 Min. : 0.0632 Min. : 0.00104 Min. : 0 Min. : 0.00000 Min. : 0.00000 Min. : 0.00 Min. : 38 Min. :-129.45458 Min. :0.000066 Min. :0.000129 Min. :0.01144 Min. : 1.076 Min. : 0.7389 Min. : 0.5959 Min. : 10 Min. :2.400e+01 Min. :1.700e+01 Min. :-3.000e+00 Min. : 0.0356 Min. : 0.00042 Min. : 0.000002 Min. : -0.035 Min. :4.200e+01 Min. :3.630e+02 Min. :5.040e+02 Min. :-1.000e+00 Min. :0.000e+00 Min. :1.830e+02 Min. : 0.00 Min. : 0.000 Min. :0.000e+00 Min. : -0.012 Min. : 0.000 Min. : 0 Min. : 1.86 Min. :-155.70141 Min. :0.000066 Min. :0.000128 Min. :0.01305 Min. : 1.072 Min. : 0.7382 Min. : 0.5955 Min. : 6.303 Min. : 3.012 Min. : 0.6818 Min. : 3.662 Min. : 0.5714 Min. : 4.58 Min. : 1.808 Min. : 2.506 Min. : 0.02436 Min. : 2.43 Min. : 4.114 Min. : 1.05 Min. : 2.941 Min. : 0.7929 Min. : 3.464 Min. : 3.537 Min. :0.7373 Min. : 1.95 Min. : 5.742 Min. : 0.02276 Min. : 2.895 Min. : 0.6671 Min. : 0.9849 Min. : 2.877 Min. : 0.4221 Min. : 2.151 Min. : 0.4344 Min. : 1.445 Min. : 0.00707 Min. : 0.7737 Min. : 0.5449 Min. : 0.3607 Min. : 0.4772 Min. : 0.8741 Min. : 2.142 Min. : 0.5863 Min. :0.6768 Min. : 0.4739 Min. : 2.887 Min. : 0.00739 Min. : 0.0 Min. : 0.0 Min. : 0.000 Min. : 0.000 Min. :0.0000 Min. :0.00000 Min. : 0.0000 Min. : 0.000 Min. : 0.00000 Min. : 0.000 Min. :7.500e+01 Min. :1.819e+03 Min. :1.334e+04 Min. :0.000e+00 Min. : 0.227 Min. : 0.017 Min. :0.000 Min. : 0.000 Min. :2.110e+02 Min. :1.092e+04 Min. :9.455e+03 Min. :0.000e+00 Min. :0.000e+00 Min. :5.359e+03 Min. : 0.061 Min. : 0.001 Min. : 0 Min. : 0.000 Min. : 0.000 Min. : 0.00 Min. : 32 Min. :-142.643 Min. :0.000 Min. :0.000 Min. :0.009 Min. : 0.930 Min. : 0.528 Min. : 0.303 Min. : 3 Min. :2.000e+00 Min. :1.000e+00 Min. :-2.000e+01 Min. : 0.035 Min. : 0.000 Min. : 0.000 Min. : -0.041 Min. :1.800e+01 Min. :7.500e+01 Min. :7.500e+01 Min. :-5.000e+00 Min. :0.000e+00 Min. :1.600e+01 Min. : 0.00 Min. : 0.00 Min. :0.000e+00 Min. : -0.015 Min. : 0.000 Min. : 0 Min. : 0.47 Min. :-162.580 Min. :0.000 Min. :0.000 Min. :0.010 Min. : 0.925 Min. : 0.527 Min. : 0.303 Min. : 4.606 Min. : 2.764 Min. : 0.706 Min. : 3.418 Min. : 0.628 Min. : 3.097 Min. : 0.794 Min. : 2.425 Min. : 0.008 Min. : 1.713 Min. : 3.401 Min. : 0.052 Min. : 2.461 Min. : 0.745 Min. : 2.414 Min. : 2.22 Min. :0.704 Min. : 1.138 Min. : 4.023 Min. : 0.000 Min. : 2.597 Min. : 0.334 Min. : 0.620 Min. : 1.906 Min. : 0.440 Min. : 2.074 Min. : 0.196 Min. : 1.001 Min. : 0.007 Min. : 0.356 Min. : 0.361 Min. : 0.051 Min. : 0.266 Min. : 0.678 Min. : 1.927 Min. : 0.27 Min. :0.625 Min. : 0.195 Min. : 2.256 Min. : 0.005 Min. : 0.0 Min. : 0.0 Min. : 0.000 Min. : 0.000 Min. :0.0000 Min. :0.00000 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: 0.88 Min. : 4.53 Min. : 0.01 Min. : 3.21 Min. : 0.78 Min. : 0.95 Min. : 1.99 Min. : 0.53 Min. : 2.33 Min. : 0.46 Min. : 1.96 Min. : 0.00 Min. : 0.78 Min. : 0.98 Min. : 0.03 Min. : 0.77 Min. : 0.64 Min. : 1.68 Min. : 0.78 Min. :0.61 Min. : 0.40 Min. : 2.61 Min. : 0.01 DELFWT:585339 PHDELWT:585339 Mode:logical Min. :0.2 Min. :1.9 Min. :1.4 Min. :1 Min. :0.4801 Min. :-1.942e-07 Min. :0.00125 Min. :0.0000016 Min. :-10.82110 Min. : 0.00718 Min. :2.8 Min. :4.05 Min. :0.5
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1st Qu.: 4.621 1st Qu.: 10.660 1st Qu.: 8.959 1st Qu.: 8.534 1st Qu.: 4.158 1st Qu.: 14.970 1st Qu.: 13.815 1st Qu.: 10.799 1st Qu.: 11.976 1st Qu.: 5.426 1st Qu.: 8.152 1st Qu.: 14.08 1st Qu.:1.305 1st Qu.: 11.216 1st Qu.: 10.265 1st Qu.: 5.811 1st Qu.: 9.308 1st Qu.: 8.383 1st Qu.: 6.265 1st Qu.: 7.426 1st Qu.: 4.056 1st Qu.: 8.088 1st Qu.: 5.805 1st Qu.: 6.302 1st Qu.: 2.814 1st Qu.: 11.637 1st Qu.: 9.125 1st Qu.: 8.735 1st Qu.: 7.873 1st Qu.: 5.209 1st Qu.: 6.744 1st Qu.: 10.56 1st Qu.:1.293 1st Qu.: 8.976 1st Qu.: 8.412 1st Qu.: 4.625 1st Qu.: 2.0 1st Qu.: 2.0 1st Qu.: 2.344 1st Qu.: 1.365 1st Qu.:0.4099 1st Qu.:0.03952 1st Qu.: 0.5484 1st Qu.: 5.243 1st Qu.: 0.03205 1st Qu.: 1.0 1st Qu.:7.513e+03 1st Qu.:1.347e+07 1st Qu.:6.791e+09 1st Qu.: 1.375e+07 1st Qu.: 0.25 1st Qu.: 0.02 1st Qu.:0.00 1st Qu.: 0.00 1st Qu.:1.827e+05 1st Qu.:5.239e+09 1st Qu.:1.094e+10 1st Qu.: 6.426e+06 1st Qu.:5.776e+05 1st Qu.:5.934e+08 1st Qu.: 0.08 1st Qu.: 0.00 1st Qu.: 0 1st Qu.: 0.00 1st Qu.: 0.00 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Median : 0 Median : 0.00 Median : 0.00 Median : 0.0 Median : 1033 Median : 0.00 Median :0.20 Median :0.42 Median :0.62 Median : 4.66 Median : 2.89 Median : 2.18 Median : 24555 Median :1.431e+08 Median :2.283e+11 Median : 1.065e+09 Median : 0.51 Median : 0.06 Median : 0.00 Median : 0.01 Median :1.294e+06 Median :2.408e+11 Median :6.220e+11 Median : 6.115e+08 Median :1.726e+08 Median :1.439e+10 Median : 0.39 Median : 0.0 Median :0.000e+00 Median : 0.00 Median : 0.00 Median : 0 Median : 677.95 Median : 0.00 Median :0.20 Median :0.42 Median :0.62 Median : 4.37 Median : 2.73 Median : 2.08 Median : 18.35 Median : 15.70 Median : 8.43 Median : 12.08 Median : 7.54 Median : 17.23 Median : 14.13 Median : 13.40 Median : 6.91 Median : 20.87 Median : 21.59 Median : 15.42 Median : 18.87 Median : 7.85 Median : 13.77 Median : 21.41 Median :1.59 Median : 17.81 Median : 15.66 Median : 9.41 Median : 14.36 Median : 12.72 Median : 8.03 Median : 10.25 Median : 6.16 Median : 13.51 Median : 11.23 Median : 10.21 Median : 5.13 Median : 17.47 Median : 16.83 Median : 13.41 Median : 15.03 Median : 7.39 Median : 11.17 Median : 17.88 Median :1.58 Median : 15.49 Median : 12.60 Median : 8.80 NA NA NA Median :0.2 Median :1.9 Median :1.4 Median :1 Median :2.0700 Median : 8.000e-13 Median :0.12257 Median :0.0150225 Median : -0.66494 Median : 1.84918 Median :2.8 Median :4.05 Median :0.5
NA NA NA 3wu2 : 431 NAG : 26360 501 : 13832 D : 38783 Mean :0.66826 Mean :0.0199 Mean :0.500310 Mean :0.06738 Mean : 13.9 Mean : 13.56 Mean : 13.12 Mean : 100.5 Mean : 96.51 Mean : 7.775 Mean : 1.195 Mean : 3.778 Mean : 0.2177 Mean : 13.89 Mean : 103 Mean : 7.918 Mean : 1.172 Mean : 3.983 Mean : 0.217 NA Mean : 0.3675 Mean : 24.14 Mean : 0.01258 Mean : 0.0042 Mean : 2.571 Mean : 0.0839 Mean : 0.0553 Mean :1.567 Mean : 0.9303 Mean : 3.465 Mean : 3.225 Mean : 35.39 Mean : 12.37 Mean : 0.1878 Mean : 5.239 Mean : 0.1462 Mean : 2.216 Mean : 3.454 Mean : 1.727 Mean :0.0001221 Mean : 1.828 Mean : 2.81 Mean : 1.616 Mean : 0.1148 Mean :0.22008 Mean : 2.813 Mean : 0.1191 Mean :0.1135 Mean : 3.217 Mean : 30.13 Mean : 1.982 Mean :1.893 Mean : 35.71 Mean : 0.68 Mean : 0.0527 Mean : 5.229 Mean : 2.002 Mean : 851.43 Mean : 17.6184 Mean :0.0234970 Mean :0.1225801 Mean :0 Mean : 1.35247 Mean : 9.750 Mean :0.222814 Mean : 0.3616 Mean : 4.372 Mean : 0.01279 Mean : 0.1215 Mean : 2.12 Mean : 2.146 Mean : 335.6 Mean : 335.6 Mean : 32.74 Mean : 17.422 Mean :0.6023 Mean :0.20956 Mean : 1.3525 Mean : 9.750 Mean : 0.21552 Mean : 1.071 Mean :1.669e+06 Mean :1.062e+13 Mean :1.785e+20 Mean :6.130e+16 Mean : 0.4920 Mean :0.06141 Mean :0.001972 Mean : 0.05614 Mean :2.952e+09 Mean :2.915e+20 Mean :8.551e+22 Mean :3.451e+16 Mean :1.665e+16 Mean :1.653e+18 Mean : 0.5528 Mean : 0.08990 Mean : 31 Mean : 0.03829 Mean : 0.02640 Mean : 0.85 Mean : 4093 Mean : 0.04576 Mean :0.244458 Mean :0.425595 Mean :0.55501 Mean : 7.997 Mean : 4.4171 Mean : 2.9344 Mean : 788428 Mean :1.342e+12 Mean :1.718e+18 Mean : 3.673e+15 Mean : 0.7472 Mean : 0.14645 Mean : 0.007762 Mean : 0.344 Mean :1.034e+09 Mean :1.362e+19 Mean :9.844e+20 Mean : 2.212e+15 Mean :1.237e+15 Mean :4.027e+16 Mean : 2.41 Mean : 0.739 Mean :1.761e+05 Mean : 0.276 Mean : 0.231 Mean : 292 Mean : 2177.81 Mean : 0.04725 Mean :0.247223 Mean :0.425723 Mean :0.55705 Mean : 7.680 Mean : 4.1928 Mean : 2.7711 Mean : 40.760 Mean : 26.090 Mean : 17.2744 Mean : 28.005 Mean : 14.9928 Mean : 34.79 Mean : 31.526 Mean : 24.292 Mean : 14.72483 Mean : 38.12 Mean : 46.273 Mean : 28.00 Mean : 41.788 Mean : 18.1950 Mean : 28.763 Mean : 43.564 Mean :1.4283 Mean : 37.48 Mean : 36.227 Mean : 20.37217 Mean : 30.192 Mean : 19.0086 Mean : 14.8229 Mean : 22.230 Mean :11.2677 Mean : 25.438 Mean : 24.6044 Mean : 17.242 Mean : 12.48289 Mean : 27.9393 Mean : 34.0886 Mean : 21.5159 Mean : 31.3630 Mean : 14.8567 Mean : 21.782 Mean : 32.1327 Mean :1.4189 Mean : 28.4110 Mean : 27.503 Mean : 16.94146 Mean : 280.4 Mean : 280.4 Mean : 25.174 Mean : 14.988 Mean :0.6539 Mean :0.19896 Mean : 1.3496 Mean : 9.719 Mean : 0.20253 Mean : 1.269 Mean :1.261e+06 Mean :5.858e+12 Mean :6.229e+19 Mean :3.420e+16 Mean : 0.531 Mean : 0.072 Mean :0.003 Mean : 0.136 Mean :2.210e+09 Mean :1.544e+20 Mean :5.458e+22 Mean :1.957e+16 Mean :9.808e+15 Mean :1.031e+18 Mean : 0.746 Mean : 0.229 Mean : 53 Mean : 0.110 Mean : 0.093 Mean : 1.41 Mean : 3176 Mean : 0.037 Mean :0.255 Mean :0.429 Mean :0.566 Mean : 7.420 Mean : 4.020 Mean : 2.658 Mean : 666175 Mean :9.895e+11 Mean :1.040e+18 Mean : 2.705e+15 Mean : 0.765 Mean : 0.154 Mean : 0.008 Mean : 0.743 Mean :8.687e+08 Mean :9.588e+18 Mean :7.258e+20 Mean : 1.676e+15 Mean :9.896e+14 Mean :3.008e+16 Mean : 2.62 Mean : 2.10 Mean :9.711e+04 Mean : 0.674 Mean : 0.628 Mean : 214 Mean : 1891.24 Mean : 0.037 Mean :0.257 Mean :0.430 Mean :0.568 Mean : 7.155 Mean : 3.838 Mean : 2.528 Mean : 35.639 Mean : 22.384 Mean : 15.906 Mean : 24.837 Mean : 13.361 Mean : 30.123 Mean : 27.194 Mean : 21.203 Mean : 13.007 Mean : 32.544 Mean : 39.928 Mean : 23.715 Mean : 35.874 Mean : 16.320 Mean : 24.835 Mean : 37.16 Mean :1.544 Mean : 31.719 Mean : 31.587 Mean : 17.298 Mean : 27.910 Mean : 17.248 Mean : 14.211 Mean : 20.708 Mean :10.646 Mean : 23.359 Mean : 22.003 Mean : 16.020 Mean : 11.288 Mean : 25.210 Mean : 30.780 Mean : 19.247 Mean : 28.110 Mean : 13.967 Mean : 19.925 Mean : 28.77 Mean :1.536 Mean : 25.184 Mean : 25.321 Mean : 14.897 Mean : 234.6 Mean : 234.6 Mean : 19.420 Mean : 12.800 Mean :0.6803 Mean :0.18783 Mean : 1.3233 Mean : 9.494 Mean : 0.18954 Mean : 1.3 Mean :1.005e+06 Mean :3.559e+12 Mean :2.610e+19 Mean : 2.044e+16 Mean : 0.57 Mean : 0.09 Mean :0.00 Mean : 0.35 Mean :1.746e+09 Mean :9.054e+19 Mean :3.549e+22 Mean : 1.201e+16 Mean :6.394e+15 Mean :6.688e+17 Mean : 1.03 Mean : 0.81 Mean : 177 Mean : 0.32 Mean : 0.29 Mean : 3.0 Mean : 2606 Mean : 0.03 Mean :0.27 Mean :0.44 Mean :0.58 Mean : 7.07 Mean : 3.78 Mean : 2.49 Mean : 585064 Mean :7.552e+11 Mean :6.637e+17 Mean : 2.064e+15 Mean : 0.77 Mean : 0.16 Mean : 0.01 Mean : 2.29 Mean :7.577e+08 Mean :7.098e+18 Mean :5.435e+20 Mean : 1.320e+15 Mean :8.244e+14 Mean :2.312e+16 Mean : 2.97 Mean : 11.3 Mean :1.465e+05 Mean : 2.35 Mean : 2.39 Mean : 250 Mean : 1717.53 Mean : 0.03 Mean :0.27 Mean :0.44 Mean :0.58 Mean : 6.83 Mean : 3.62 Mean : 2.38 Mean : 32.47 Mean : 20.00 Mean : 15.15 Mean : 22.96 Mean : 12.35 Mean : 27.20 Mean : 24.29 Mean : 19.24 Mean : 11.81 Mean : 28.87 Mean : 35.71 Mean : 20.90 Mean : 31.94 Mean : 15.19 Mean : 22.42 Mean : 32.92 Mean :1.66 Mean : 27.90 Mean : 28.75 Mean : 15.29 Mean : 26.74 Mean : 16.25 Mean : 14.06 Mean : 20.01 Mean :10.36 Mean : 22.23 Mean : 20.44 Mean : 15.37 Mean : 10.61 Mean : 23.57 Mean : 28.80 Mean : 17.89 Mean : 26.14 Mean : 13.57 Mean : 18.93 Mean : 26.69 Mean :1.65 Mean : 23.15 Mean : 24.20 Mean : 13.65 NA NA NA Mean :0.2 Mean :1.9 Mean :1.4 Mean :1 Mean :2.1485 Mean : 4.470e-11 Mean :0.12905 Mean :0.0195631 Mean : -0.70075 Mean : 2.60360 Mean :2.8 Mean :4.05 Mean :0.5
NA NA NA 4pj0 : 353 CL : 23223 302 : 11002 E : 16206 3rd Qu.:0.86957 3rd Qu.:0.0000 3rd Qu.:0.713459 3rd Qu.:0.00000 3rd Qu.: 20.0 3rd Qu.: 19.00 3rd Qu.: 18.00 3rd Qu.: 133.0 3rd Qu.:126.00 3rd Qu.:10.000 3rd Qu.: 1.000 3rd Qu.: 5.000 3rd Qu.: 0.0000 3rd Qu.: 20.00 3rd Qu.: 136 3rd Qu.:10.000 3rd Qu.: 1.000 3rd Qu.: 6.000 3rd Qu.: 0.000 NA 3rd Qu.: 0.0000 3rd Qu.: 32.00 3rd Qu.: 0.00000 3rd Qu.: 0.0000 3rd Qu.: 0.000 3rd Qu.: 0.0000 3rd Qu.: 0.0000 3rd Qu.:1.962 3rd Qu.: 0.0000 3rd Qu.: 0.000 3rd Qu.: 0.000 3rd Qu.: 40.00 3rd Qu.: 17.00 3rd Qu.: 0.0000 3rd Qu.: 1.000 3rd Qu.: 0.0000 3rd Qu.: 0.000 3rd Qu.: 0.000 3rd Qu.: 0.000 3rd Qu.:0.0000000 3rd Qu.: 1.000 3rd Qu.: 0.00 3rd Qu.: 2.000 3rd Qu.: 0.0000 3rd Qu.:0.25000 3rd Qu.: 1.000 3rd Qu.: 0.0000 3rd Qu.:0.0000 3rd Qu.: 3.000 3rd Qu.: 35.00 3rd Qu.: 2.000 3rd Qu.:2.000 3rd Qu.: 40.00 3rd Qu.: 0.00 3rd Qu.: 0.0000 3rd Qu.: 11.000 3rd Qu.: 2.000 3rd Qu.: 782.08 3rd Qu.: 19.5602 3rd Qu.:0.0285268 3rd Qu.:0.1433580 3rd Qu.:0 3rd Qu.: 1.50809 3rd Qu.: 11.274 3rd Qu.:0.256536 3rd Qu.: 0.0240 3rd Qu.: 6.000 3rd Qu.: 0.00000 3rd Qu.: 0.0000 3rd Qu.: 3.00 3rd Qu.: 3.000 3rd Qu.: 139.0 3rd Qu.: 139.0 3rd Qu.: 34.18 3rd Qu.: 18.981 3rd Qu.:0.7187 3rd Qu.:0.23400 3rd Qu.: 1.5081 3rd Qu.: 11.274 3rd Qu.: 0.23399 3rd Qu.: 1.000 3rd Qu.:5.838e+05 3rd Qu.:7.047e+10 3rd Qu.:2.030e+15 3rd Qu.:5.090e+12 3rd Qu.: 0.6068 3rd Qu.:0.06885 3rd Qu.:0.001749 3rd Qu.: 0.03164 3rd Qu.:1.272e+08 3rd Qu.:1.848e+15 3rd Qu.:8.092e+15 3rd Qu.:3.046e+12 3rd Qu.:1.290e+12 3rd Qu.:3.792e+13 3rd Qu.: 0.5898 3rd Qu.: 0.03465 3rd Qu.: 0 3rd Qu.: 0.01957 3rd Qu.: 0.00950 3rd Qu.: 0.19 3rd Qu.: 4273 3rd Qu.: 0.82325 3rd Qu.:0.360112 3rd Qu.:0.618134 3rd Qu.:0.74494 3rd Qu.: 9.976 3rd Qu.: 5.2246 3rd Qu.: 3.4617 3rd Qu.: 257105 3rd Qu.:1.350e+10 3rd Qu.:1.686e+14 3rd Qu.: 1.066e+12 3rd Qu.: 0.9580 3rd Qu.: 0.17921 3rd Qu.: 0.007750 3rd Qu.: 0.127 3rd Qu.:5.470e+07 3rd Qu.:3.311e+14 3rd Qu.:1.542e+15 3rd Qu.: 7.112e+11 3rd Qu.:3.701e+11 3rd Qu.:7.337e+12 3rd Qu.: 1.52 3rd Qu.: 0.237 3rd Qu.:1.000e+00 3rd Qu.: 0.085 3rd Qu.: 0.049 3rd Qu.: 1 3rd Qu.: 2372.66 3rd Qu.: 0.88385 3rd Qu.:0.369874 3rd Qu.:0.622709 3rd Qu.:0.75041 3rd Qu.: 9.651 3rd Qu.: 4.9417 3rd Qu.: 3.2234 3rd Qu.: 52.866 3rd Qu.: 31.939 3rd Qu.: 21.8753 3rd Qu.: 36.564 3rd Qu.: 19.4280 3rd Qu.: 44.81 3rd Qu.: 41.863 3rd Qu.: 30.606 3rd Qu.: 19.06451 3rd Qu.: 47.73 3rd Qu.: 60.562 3rd Qu.: 35.18 3rd Qu.: 55.044 3rd Qu.: 24.1803 3rd Qu.: 37.437 3rd Qu.: 56.348 3rd Qu.:1.5706 3rd Qu.: 49.14 3rd Qu.: 47.297 3rd Qu.: 27.11986 3rd Qu.: 38.983 3rd Qu.: 23.7998 3rd Qu.: 18.7919 3rd Qu.: 28.952 3rd Qu.:14.4114 3rd Qu.: 32.914 3rd Qu.: 33.3966 3rd Qu.: 21.831 3rd Qu.: 17.08645 3rd Qu.: 34.6093 3rd Qu.: 44.8828 3rd Qu.: 27.5450 3rd Qu.: 41.6574 3rd Qu.: 19.3818 3rd Qu.: 28.500 3rd Qu.: 41.2626 3rd Qu.:1.5651 3rd Qu.: 36.8588 3rd Qu.: 35.746 3rd Qu.: 22.85958 3rd Qu.: 93.0 3rd Qu.: 93.0 3rd Qu.: 25.520 3rd Qu.: 16.495 3rd Qu.:0.7851 3rd Qu.:0.22254 3rd Qu.: 1.5081 3rd Qu.: 11.274 3rd Qu.: 0.21924 3rd Qu.: 1.000 3rd Qu.:3.756e+05 3rd Qu.:2.868e+10 3rd Qu.:5.208e+14 3rd Qu.:2.038e+12 3rd Qu.: 0.667 3rd Qu.: 0.079 3rd Qu.:0.002 3rd Qu.: 0.045 3rd Qu.:7.356e+07 3rd Qu.:5.827e+14 3rd Qu.:2.802e+15 3rd Qu.:1.291e+12 3rd Qu.:5.968e+11 3rd Qu.:1.427e+13 3rd Qu.: 0.730 3rd Qu.: 0.048 3rd Qu.: 0 3rd Qu.: 0.029 3rd Qu.: 0.015 3rd Qu.: 0.27 3rd Qu.: 3227 3rd Qu.: 0.635 3rd Qu.:0.394 3rd Qu.:0.636 3rd Qu.:0.760 3rd Qu.: 9.416 3rd Qu.: 4.795 3rd Qu.: 3.210 3rd Qu.: 186793 3rd Qu.:7.090e+09 3rd Qu.:6.547e+13 3rd Qu.: 5.511e+11 3rd Qu.: 0.963 3rd Qu.: 0.173 3rd Qu.: 0.007 3rd Qu.: 0.141 3rd Qu.:3.616e+07 3rd Qu.:1.369e+14 3rd Qu.:6.996e+14 3rd Qu.: 3.841e+11 3rd Qu.:2.083e+11 3rd Qu.:3.536e+12 3rd Qu.: 1.58 3rd Qu.: 0.23 3rd Qu.:1.000e+00 3rd Qu.: 0.098 3rd Qu.: 0.059 3rd Qu.: 1 3rd Qu.: 2083.69 3rd Qu.: 0.666 3rd Qu.:0.402 3rd Qu.:0.640 3rd Qu.:0.765 3rd Qu.: 9.149 3rd Qu.: 4.545 3rd Qu.: 3.005 3rd Qu.: 46.209 3rd Qu.: 27.756 3rd Qu.: 19.533 3rd Qu.: 32.115 3rd Qu.: 17.451 3rd Qu.: 39.205 3rd Qu.: 36.314 3rd Qu.: 27.054 3rd Qu.: 16.816 3rd Qu.: 41.186 3rd Qu.: 52.755 3rd Qu.: 30.310 3rd Qu.: 47.677 3rd Qu.: 21.542 3rd Qu.: 32.552 3rd Qu.: 48.68 3rd Qu.:1.734 3rd Qu.: 42.123 3rd Qu.: 41.080 3rd Qu.: 23.333 3rd Qu.: 35.907 3rd Qu.: 22.303 3rd Qu.: 17.289 3rd Qu.: 26.491 3rd Qu.:13.615 3rd Qu.: 30.436 3rd Qu.: 30.480 3rd Qu.: 20.483 3rd Qu.: 15.551 3rd Qu.: 31.909 3rd Qu.: 41.355 3rd Qu.: 25.439 3rd Qu.: 38.113 3rd Qu.: 17.988 3rd Qu.: 26.125 3rd Qu.: 37.90 3rd Qu.:1.730 3rd Qu.: 33.578 3rd Qu.: 32.648 3rd Qu.: 20.670 3rd Qu.: 61.0 3rd Qu.: 61.0 3rd Qu.: 19.248 3rd Qu.: 14.201 3rd Qu.:0.8471 3rd Qu.:0.21062 3rd Qu.: 1.5076 3rd Qu.: 11.274 3rd Qu.: 0.20395 3rd Qu.: 1.0 3rd Qu.:2.791e+05 3rd Qu.:1.547e+10 3rd Qu.:2.022e+14 3rd Qu.: 1.130e+12 3rd Qu.: 0.72 3rd Qu.: 0.09 3rd Qu.:0.00 3rd Qu.: 0.06 3rd Qu.:5.227e+07 3rd Qu.:2.730e+14 3rd Qu.:1.473e+15 3rd Qu.: 7.446e+11 3rd Qu.:3.643e+11 3rd Qu.:7.601e+12 3rd Qu.: 0.86 3rd Qu.: 0.06 3rd Qu.: 0 3rd Qu.: 0.04 3rd Qu.: 0.02 3rd Qu.: 0.3 3rd Qu.: 2631 3rd Qu.: 0.48 3rd Qu.:0.43 3rd Qu.:0.66 3rd Qu.:0.78 3rd Qu.: 9.20 3rd Qu.: 4.53 3rd Qu.: 3.05 3rd Qu.: 156259 3rd Qu.:4.929e+09 3rd Qu.:3.743e+13 3rd Qu.: 3.971e+11 3rd Qu.: 0.96 3rd Qu.: 0.16 3rd Qu.: 0.01 3rd Qu.: 0.15 3rd Qu.:2.917e+07 3rd Qu.:8.334e+13 3rd Qu.:4.729e+14 3rd Qu.: 2.803e+11 3rd Qu.:1.575e+11 3rd Qu.:2.401e+12 3rd Qu.: 1.59 3rd Qu.: 0.2 3rd Qu.:1.000e+00 3rd Qu.: 0.10 3rd Qu.: 0.06 3rd Qu.: 1 3rd Qu.: 1929.17 3rd Qu.: 0.49 3rd Qu.:0.44 3rd Qu.:0.66 3rd Qu.:0.78 3rd Qu.: 8.95 3rd Qu.: 4.30 3rd Qu.: 2.86 3rd Qu.: 42.05 3rd Qu.: 25.06 3rd Qu.: 17.96 3rd Qu.: 29.34 3rd Qu.: 16.15 3rd Qu.: 35.50 3rd Qu.: 32.53 3rd Qu.: 24.70 3rd Qu.: 15.11 3rd Qu.: 36.88 3rd Qu.: 47.74 3rd Qu.: 27.00 3rd Qu.: 42.85 3rd Qu.: 19.77 3rd Qu.: 29.38 3rd Qu.: 43.77 3rd Qu.:1.88 3rd Qu.: 37.55 3rd Qu.: 37.15 3rd Qu.: 20.64 3rd Qu.: 34.37 3rd Qu.: 21.46 3rd Qu.: 16.53 3rd Qu.: 25.24 3rd Qu.:13.23 3rd Qu.: 29.13 3rd Qu.: 28.83 3rd Qu.: 19.79 3rd Qu.: 14.50 3rd Qu.: 30.37 3rd Qu.: 39.48 3rd Qu.: 24.14 3rd Qu.: 36.12 3rd Qu.: 17.24 3rd Qu.: 24.81 3rd Qu.: 36.04 3rd Qu.:1.88 3rd Qu.: 31.67 3rd Qu.: 30.96 3rd Qu.: 19.25 NA NA NA 3rd Qu.:0.2 3rd Qu.:1.9 3rd Qu.:1.4 3rd Qu.:1 3rd Qu.:2.4793 3rd Qu.: 5.010e-11 3rd Qu.:0.15923 3rd Qu.:0.0253543 3rd Qu.: -0.50039 3rd Qu.: 3.07809 3rd Qu.:2.8 3rd Qu.:4.05 3rd Qu.:0.5
NA NA NA 4rku : 352 CA : 21038 402 : 9644 F : 12241 Max. :1.00000 Max. :0.9989 Max. :1.000000 Max. :1.00000 Max. :178.0 Max. :111.00 Max. :111.00 Max. :1848.0 Max. :858.00 Max. :84.000 Max. :28.000 Max. :61.000 Max. :13.0000 Max. :128.00 Max. :1223 Max. :93.000 Max. :28.000 Max. :55.000 Max. :13.000 NA Max. :2792.0000 Max. :671.00 Max. :150.00000 Max. :54.0000 Max. :417.000 Max. :907.0000 Max. :957.0000 Max. :6.459 Max. :492.0000 Max. :779.000 Max. :2579.000 Max. :9934.00 Max. :336.00 Max. :1863.0000 Max. :1009.000 Max. :2998.0000 Max. :434.000 Max. :304.000 Max. :569.000 Max. :0.4666667 Max. :572.000 Max. :129.00 Max. :251.000 Max. :204.0000 Max. :1.00000 Max. :864.000 Max. :228.0000 Max. :1.0000 Max. :85.000 Max. :877.00 Max. :157.000 Max. :5.000 Max. :3801.00 Max. :6134.00 Max. :320.0000 Max. :310.000 Max. :101.000 Max. :90952.51 Max. :442.4445 Max. :0.4264420 Max. :1.9595600 Max. :0 Max. :44.63360 Max. :173.252 Max. :4.035155 Max. :53.5040 Max. :163.000 Max. :14.00000 Max. :15.0000 Max. :76.00 Max. :41.000 Max. :114577.0 Max. :114577.0 Max. :2427.94 Max. :441.137 Max. :8.5969 Max. :8.01064 Max. :44.6336 Max. :173.252 Max. :10.50955 Max. :28.000 Max. :2.629e+09 Max. :4.338e+17 Max. :2.908e+25 Max. :8.380e+21 Max. :39.6469 Max. :6.01088 Max. :0.411045 Max. :189.35070 Max. :1.633e+14 Max. :3.583e+25 Max. :2.665e+28 Max. :4.527e+21 Max. :1.957e+21 Max. :3.743e+23 Max. :2760.5726 Max. :303.90731 Max. :7617375 Max. :189.07167 Max. :188.88566 Max. :109414.70 Max. :303493 Max. : 70.04396 Max. :0.994777 Max. :1.000000 Max. :1.00000 Max. :202.762 Max. :34.5164 Max. :20.3426 Max. :567372596 Max. :2.006e+16 Max. :1.808e+23 Max. : 4.049e+20 Max. :412.3283 Max. :32.92662 Max. :27.995065 Max. :29271.048 Max. :1.282e+13 Max. :1.509e+24 Max. :1.643e+26 Max. : 2.342e+20 Max. :1.203e+20 Max. :5.255e+21 Max. :298590.95 Max. :7616.409 Max. :8.912e+10 Max. :29231.847 Max. :29205.712 Max. :123080053 Max. :55142.18 Max. : 89.96053 Max. :0.995899 Max. :1.000000 Max. :1.00000 Max. 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NA NA NA (Other):582054 (Other):386715 (Other):501104 (Other): 59264 NA NA NA NA NA NA NA NA NA NA NA NA NA NA’s :11007 NA’s :11007 NA’s :11007 NA’s :11007 NA’s :11007 NA’s :11007 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA’s :2 NA NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA’s :7 NA NA NA NA NA NA NA NA NA’s :13 NA NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA’s :5482 NA NA NA NA NA NA NA NA NA’s :27 NA NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA’s :40036 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Dalsze przetwarzanie i analiza danych

Ograniczenie liczby klas (res_name) do 50 najpopularniejszych wartości

top50 <- All_Data %>% group_by(res_name) %>% summarise(n = n()) %>% arrange(desc(n)) %>% head(50)
top50 <- as.array(top50$res_name)
kable(top50)%>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "600px")
x
SO4
GOL
EDO
NAG
CL
CA
ZN
MG
HEM
PO4
NA
ACT
DMS
IOD
PEG
CLA
K
FAD
NAD
MN
ADP
MLY
NAP
CD
MPD
FMT
MAN
PG4
MES
CU
ATP
COA
1PE
BR
NDP
FMN
EPE
HEC
PGE
TRS
SF4
NI
ACY
FE
NO3
PLP
GDP
SAH
FE2
SEP 
All_Data <- All_Data %>% filter(res_name %in% top50)

Liczba przykładów

examples <- All_Data %>% group_by(res_name) %>% summarise(n = n()) %>% arrange(desc(n))
kable(examples)%>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "600px")
res_name n
SO4 56572
GOL 40606
EDO 30825
NAG 26360
CL 23223
CA 21038
ZN 19826
MG 14779
HEM 11192
PO4 11090
NA 9613
ACT 8096
DMS 6633
IOD 6317
PEG 4987
CLA 4784
K 4706
FAD 4555
NAD 4501
MN 4215
ADP 3819
MLY 3509
NAP 3505
CD 3242
MPD 3221
FMT 2918
MAN 2841
PG4 2768
MES 2697
CU 2353
ATP 2296
COA 2183
1PE 2136
BR 2127
NDP 2106
FMN 2084
EPE 1933
HEC 1917
PGE 1905
TRS 1656
SF4 1647
NI 1637
ACY 1609
FE 1602
NO3 1596
PLP 1594
GDP 1589
SAH 1587
FE2 1560
SEP 1491

Wykresy rozkładów liczby atomów (local_res_atom_non_h_count) i elektronów (local_res_atom_non_h_electron_sum)

ggplot(All_Data, aes(local_res_atom_non_h_count)) + geom_histogram(binwidth = 1)

ggplot(All_Data, aes(local_res_atom_non_h_electron_sum)) + geom_histogram(binwidth = 1)

10 klas z największą niezgodnością liczby atomów

atom_count_diff <-  All_Data %>% 
  mutate(diff = abs(local_res_atom_non_h_count - dict_atom_non_h_count)) %>% 
  group_by(res_name) %>% 
  summarise(mean_diff = mean(diff), sd_diff = sd(diff), min_diff = min(diff), max_diff = max(diff), n=n(), n_diff = sum(diff>0)) %>% 
  mutate(percent_diff = n_diff/n * 100) %>% 
  select(res_name, percent_diff) %>% 
  arrange(desc(percent_diff)) %>% 
  head(10) %>% 
  transmute(res_name, percent_diff = round(percent_diff, 2))

kable(atom_count_diff)%>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "600px")
res_name percent_diff
MLY 99.29
NAG 97.65
SEP 97.05
MAN 89.19
PLP 85.45
1PE 45.88
CLA 34.01
PG4 23.19
COA 9.62
NAP 8.73

10 klas z największą niezgodnością liczby elektronów

electron_count_diff <- All_Data %>% 
  mutate(diff = abs(local_res_atom_non_h_electron_sum - dict_atom_non_h_electron_sum)) %>% 
  group_by(res_name) %>% 
  summarise(mean_diff = mean(diff), sd_diff = sd(diff), min_diff = min(diff), max_diff = max(diff), n=n(), n_diff = sum(diff>0)) %>% 
  mutate(percent_diff = n_diff/n * 100) %>% 
  select(res_name, percent_diff) %>% 
  arrange(desc(percent_diff)) %>% 
  head(10) %>% 
  transmute(res_name, percent_diff = round(percent_diff, 2))

kable(electron_count_diff)%>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "600px")
res_name percent_diff
MLY 99.29
NAG 97.65
SEP 97.05
MAN 89.19
PLP 85.45
1PE 45.88
CLA 34.01
PG4 23.19
COA 9.62
NAP 8.73

Prezentacja niezgodności liczby elektronów

plot_ly(All_Data, x = ~local_res_atom_non_h_electron_sum, y = ~dict_atom_non_h_electron_sum, type="scattergl", mode="markers")

Prezentacja niezgodności liczby atomów

plot_ly(All_Data, x = ~local_res_atom_non_h_count, y = ~dict_atom_non_h_count, type="scattergl", mode="markers")

Rozkład wartości wszystkich kolumn zaczynających się od part_01

columns_part01 <- colnames(All_Data)
columns_part01 <- columns_part01[startsWith(columns_part01, "part_01")]
columns_part01 <- head(columns_part01, 10)
columns_part01_all_data <- All_Data %>% select(columns_part01)

data_gathered <- gather(columns_part01_all_data)
data_gathered_means <- data_gathered %>% group_by(key) %>% summarise(mean.value = mean(value, na.rm = TRUE))
ggplot(data_gathered, aes(value)) + 
  geom_histogram(bins = 10)  +
  facet_wrap(~key, scales = 'free_x',ncol = 2) + 
  geom_vline(data = data_gathered_means,aes(xintercept = mean.value), color="red", linetype="dashed", size=1) +
  geom_text(data = data_gathered_means, aes(label=round(mean.value,2) ,y=0,x=mean.value), vjust=-1,col='orange',size=5)
## Warning: Removed 7 rows containing non-finite values (stat_bin).

Przygotowanie danych do regresji i klasyfikacji

manipulation_data <- All_Data
columns_to_predict <- colnames(manipulation_data)
columns_to_predict <- columns_to_predict[startsWith(columns_to_predict, "part_")]
manipulation_data <- manipulation_data %>% select(columns_to_predict)
numeric_data <- sapply(manipulation_data, class)
numeric_data <- numeric_data == "numeric" | numeric_data == "integer"
numeric_data <- manipulation_data[, numeric_data]
label_attributes <- c("local_res_atom_non_h_count", "local_res_atom_non_h_electron_sum", "res_name")
numeric_data[label_attributes] <- All_Data[label_attributes]
numeric_data <- numeric_data[complete.cases(numeric_data), ]
label_store <- numeric_data %>% select(label_attributes)
numeric_data <- numeric_data %>% select(-label_attributes)

q <- sapply(numeric_data, quantile, c(.05, .95) )
numeric_data <- as.data.frame(sapply(numeric_data, squish, q))

numeric_data <- numeric_data[ ,sapply(numeric_data, function(x) sd(quantile(x,c(.25, .75))) ) >0.1]
tmp <- cor(numeric_data)
#show cors
tmp2 <- tmp
tmp2[upper.tri(tmp2)] <- NA
diag(tmp2) <- NA
best_cor <- as.data.frame(as.table(tmp2)) %>% filter(!is.na(Freq)) %>% arrange(desc(abs(Freq))) %>% head(3)
for(d in c(1,2,3)) {
  data_x_column <- as.character( best_cor[d,"Var1"])
  data_y_column <- as.character( best_cor[d,"Var2"])
  #print(paste(data_x_column,data_y_column))
  print(qplot(x=numeric_data[,data_x_column], y=numeric_data[,data_y_column]))
}

worst_cor <- as.data.frame(as.table(tmp2)) %>% filter(!is.na(Freq)) %>% arrange(desc(abs(Freq))) %>% tail(3)
for(d in c(1,2,3)) {
  data_x_column <- as.character( worst_cor[d,"Var1"])
  data_y_column <- as.character( worst_cor[d,"Var2"])
  print(qplot(x=numeric_data[,data_x_column], y=numeric_data[,data_y_column]))
}

tmp[upper.tri(tmp)] <- 0
diag(tmp) <- 0
cols_to_drop <- apply(tmp,2,function(x) any(abs(x) > 0.85))
data.new <- numeric_data[,!cols_to_drop]
data.new[label_attributes] <- label_store[label_attributes]
kable(summary(data.new))%>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "600px")
part_01_density_Z_1_0 part_02_density_segments_count part_02_max_over_std part_02_shape_M000 part_02_density_M000 part_02_density_Z_1_0 part_02_density_Z_4_0 local_res_atom_non_h_count local_res_atom_non_h_electron_sum 
res_name </th>
Min. :1.000 Min. : 1.0 Min. : 3.834 Min. : 32.0 Min. : 2.538 Min. :1.000 Min. : 1.000 Min. : 1.000 Min. : 6.00 SO4 : 55070
1st Qu.:1.352 1st Qu.: 2.0 1st Qu.: 5.621 1st Qu.: 372.0 1st Qu.: 222.974 1st Qu.:1.422 1st Qu.: 2.877 1st Qu.: 1.000 1st Qu.: 28.00 GOL : 37281
Median :1.536 Median : 6.0 Median : 7.598 Median : 874.0 Median : 576.064 Median :1.668 Median : 7.239 Median : 5.000 Median : 42.00 EDO : 27993
Mean :1.549 Mean : 116.6 Mean : 10.343 Mean : 927.2 Mean : 755.862 Mean :1.703 Mean : 11.789 Mean : 9.053 Mean : 71.56 CL : 22692
3rd Qu.:1.734 3rd Qu.: 27.0 3rd Qu.: 12.095 3rd Qu.:1641.0 3rd Qu.:1401.333 3rd Qu.:1.906 3rd Qu.: 15.113 3rd Qu.: 7.000 3rd Qu.: 53.00 NAG : 22383
Max. :3.423 Max. :1641.0 Max. :133.748 Max. :1641.0 Max. :1641.000 Max. :5.051 Max. :107.269 Max. :65.000 Max. :410.00 CA : 20664
NA NA NA NA NA NA NA NA NA (Other):171859

Przewidywanie liczby atomów

columns_part_all_data_predict <- data.new %>% select(-c("local_res_atom_non_h_electron_sum", "res_name"))

inTraining <- 
  createDataPartition(
    y = columns_part_all_data_predict$local_res_atom_non_h_count,
    p = .75,
    list = FALSE)

training <- columns_part_all_data_predict[ inTraining,]
testing  <- columns_part_all_data_predict[-inTraining,]

ctrl <- trainControl(method = "none")

fit_atom <- train(local_res_atom_non_h_count ~ .,
     data = training,
     method = "lm",
     trControl = ctrl)

rfClasses <- predict(fit_atom, newdata = testing)
kable(postResample(rfClasses,testing$local_res_atom_non_h_count ))%>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "600px")
x
RMSE 9.845877
Rsquared 0.432954
MAE 5.518124

Przewidywanie liczby elektronów

columns_part_all_data_predict <- data.new %>% select(-c("local_res_atom_non_h_count", "res_name"))

inTraining <- 
  createDataPartition(
    y = columns_part_all_data_predict$local_res_atom_non_h_electron_sum,
    p = .75,
    list = FALSE)

training <- columns_part_all_data_predict[ inTraining,]
testing  <- columns_part_all_data_predict[-inTraining,]

ctrl <- trainControl(method = "none")

fit_electron <- train(local_res_atom_non_h_electron_sum ~ .,
     data = training,
     method = "lm",
     trControl = ctrl)

rfClasses <- predict(fit_electron, newdata = testing)
kable(postResample(rfClasses,testing$local_res_atom_non_h_electron_sum ))%>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "600px")
x
RMSE 67.0337991
Rsquared 0.4213179
MAE 37.7833663

Przewidywanie atrybutu res_name

columns_part_all_data_predict <- data.new %>% select(-c("local_res_atom_non_h_count", "local_res_atom_non_h_electron_sum"))
columns_part_all_data_predict$res_name <- droplevels(columns_part_all_data_predict$res_name)
inTraining <- 
  createDataPartition(
    y = columns_part_all_data_predict$res_name,
    p = .75,
    list = FALSE)

training <- columns_part_all_data_predict[ inTraining,]
testing  <- columns_part_all_data_predict[-inTraining,]

ctrl <- trainControl(method = "none")

fit <- train(res_name ~ .,
     data = training,
     method = "rf",
     trControl = ctrl,
     ntree = 4)

rfClasses <- predict(fit, newdata = testing)
print(confusionMatrix(rfClasses,testing$res_name )$overall['Accuracy'] )
##  Accuracy 
## 0.2736408
columns_part_all_data_predict <- data.new %>% select(-c("local_res_atom_non_h_count", "local_res_atom_non_h_electron_sum"))
columns_part_all_data_predict$res_name <- droplevels(columns_part_all_data_predict$res_name)

predicted_electrons <- predict(fit_electron, newdata = columns_part_all_data_predict %>% select(-c( "res_name")))
predicted_atoms <- predict(fit_atom, newdata = columns_part_all_data_predict %>% select(-c("res_name")))

columns_part_all_data_predict <- data.frame(res_name = columns_part_all_data_predict$res_name, electrons = predicted_electrons, atoms = predicted_atoms)

inTraining <- 
  createDataPartition(
    y = columns_part_all_data_predict$res_name,
    p = .75,
    list = FALSE)

training <- columns_part_all_data_predict[ inTraining,]
testing  <- columns_part_all_data_predict[-inTraining,]

ctrl <- trainControl(method = "none")

fit <- train(res_name ~ .,
     data = training,
     method = "rf",
     trControl = ctrl,
     ntree = 4)

rfClasses <- predict(fit, newdata = testing)
print(confusionMatrix(rfClasses,testing$res_name )$overall['Accuracy'])
##  Accuracy 
## 0.1631494